PSY 406 Final Study Guide

What is a concept? What is the difference between an artificial concept and a natural concept? Which of these would someone be likely to be successful using rules/definitions to determine category membership?

A concept is a mental representation of a class or individual that captures the meaning of objects, events, and abstract ideas. Artificial concepts have clear rules that determine category membership (like mathematical shapes), while natural concepts have fuzzy boundaries and varied examples (like "furniture"). Rules/definitions work effectively for artificial concepts because they have necessary and sufficient conditions without exceptions, whereas natural concepts resist simple definition.

What is meant by family resemblance for membership in a natural category?

Family resemblance refers to how members of natural categories share overlapping features without any single feature being necessary for membership. Like family members who may share various combinations of traits (nose shape, eye color, etc.), category members share similarities, but no single defining feature must be present in all members. For example, chairs might have backs, seats, legs, and support weight, but bean bag chairs lack some features yet remain chairs through overall resemblance to the category.

What would an incremental learning approach predict about performance in learning a category over time? What would a hypothesis testing approach predict about performance in learning a category over time? 

An incremental learning approach predicts gradual improvement in category learning, with performance steadily increasing over time as associations strengthen with each exposure. When correctly categorizing items, associations increase; when incorrectly categorizing, associations decrease.

A hypothesis testing approach predicts that performance will remain at chance level until the correct rule is discovered, at which point performance jumps to perfect or near-perfect accuracy. This approach shows no improvement until the "aha" moment when the correct hypothesis is formed.

What is a prototype view of categorization? What is an exemplar view? What do they predict about categorization, and what evidence supports each view?

The prototype view proposes that we categorize by comparing new items to a single prototype (either the best example or central tendency/average) of the category. It predicts faster and more accurate classification of prototypical items even if never seen before.

The exemplar view suggests we store and compare new items to multiple specific examples from a category. It predicts better performance with previously encountered instances and sensitivity to specific variations within categories.

Evidence supporting prototype view: Prototypes are categorized more rapidly even when never seen before, and prototype recognition shows little forgetting after delays. Evidence supporting exemplar view: People are sensitive to specific instances and to category variability, and can handle exceptions and edge cases within categories.

What does the embodied / grounded cognition approach predict about how we represent categories? 

The embodied/grounded cognition approach predicts that category representations activate the same neural regions used during actual experience with category members. Concepts function as "simulators" that activate sensory, motor, and emotional systems associated with experiencing the concept. For example, reading action words like "kick" activates motor regions for leg movement, and object size affects corresponding motor actions (small objects prime small motor movements). The "hub and spoke" model suggests the anterior temporal lobe coordinates conceptual knowledge across different modalities.

What is the "hub and spoke" model of embodied cognition?

The "hub and spoke" model of embodied cognition proposes that conceptual knowledge is organized with the anterior temporal lobe acting as a central hub that coordinates information across different modalities (the spokes). The hub integrates sensory, motor, and emotional information from distributed brain regions. When this hub is disrupted through techniques like transcranial magnetic stimulation, performance on conceptual tasks is severely impaired, while disrupting individual spokes only affects specific types of judgments. This model explains how we can access conceptual knowledge through multiple pathways and how damage to the hub can cause widespread conceptual impairments.

What is meant by language as a joint activity? What is common ground, and how do people establish common ground when communicating?

Language as a joint activity refers to communication being a collaborative process where two or more people work together to achieve shared understanding. Common ground is the knowledge, beliefs, and assumptions shared between speakers, including background knowledge, current conversational context, and shared experiences. People establish common ground through a process called "grounding" which involves presentation (offering information) and acceptance phases (confirming understanding). As common ground increases through repeated interactions, communication becomes more efficient, requiring fewer words and resulting in better performance, as demonstrated in director-matcher card arrangement experiments.

What does learning of common ground among amnesiacs and healthy controls tell us about the nature of common ground in memory?

Studies with amnesiacs and healthy controls reveal that common ground depends on both explicit and implicit memory systems. Even amnesiacs with impaired explicit memory showed improved communication efficiency over repeated interactions, suggesting common ground partially relies on implicit memory processes. However, healthy controls with intact explicit memory showed additional benefits, indicating common ground utilizes both memory systems. This explains how we can build common ground with someone even when we can't explicitly recall all details of previous interactions.

What is a phoneme? Morpheme? Word?

A phoneme is the smallest unit of sound in language that can distinguish meaning (e.g., /b/, /p/, /t/ in "bit"). A morpheme is the smallest unit that carries meaning (e.g., "table" is one morpheme, while "bark-ing" has two). A word is a combination of one or more morphemes that functions as a basic unit of syntax and meaning in language.

Do pauses between words help us segment speech when we hear it? 

Pauses between words don't reliably help us segment speech, as natural speech rarely contains consistent pauses between words. Instead, we rely on multiple cues including stress patterns, pitch variations, and crucially, context and statistical probabilities of syllable transitions. Context plays a vital role in identifying ambiguous sounds or words within sentences.

How does context in a sentence help us understand words or phonemes? How does context of a word help us identify letters? What is the word frequency effect? How are these examples of top-down processing?

Context in sentences helps us understand words through top-down processing. In the phonemic restoration effect, listeners "hear" sounds covered by noise when context suggests what should be there (e.g., hearing "wave" in "There was time to *ave our friend goodbye"). Similarly, the word superiority effect shows letters are identified more accurately when embedded in words than in isolation, demonstrating how word context aids letter identification. The word frequency effect shows we process common words faster than rare ones because our prior experience creates expectations. These are all examples of top-down processing where higher-level knowledge (sentence meaning, word recognition, frequency statistics) influences lower-level perception.

How does statistical learning of common syllabus transitions help us segment speech into words?

Statistical learning helps us segment speech by tracking probabilities of syllable transitions. After exposure to a stream of speech, we detect which syllables commonly occur together (high transitional probability) versus which rarely occur together (low transitional probability). Even 8-month-old infants demonstrate this ability - they look longer at unexpected syllable combinations compared to expected ones after brief exposure to speech streams. For example, after hearing "golabu" repeatedly, infants learn these syllables belong together, and they notice when the sequence is broken. This statistical learning mechanism helps us identify word boundaries without relying on pauses, which are often absent in natural speech.

What is an anaphoric inference? Instrument inference? Causal inference?

An anaphoric inference involves determining what a pronoun or noun phrase refers to from earlier in the text. For example, in "Lisa's poodle Riffifi won the dog show. She eats a specialized diet," readers must infer that "she" refers to the dog Riffifi, not Lisa. This connects elements across sentences.

An instrument inference involves deducing the means or tool used to accomplish an action when not explicitly stated. For example, reading "John cut the birthday cake" leads to inferring he used a knife, though no specific instrument was mentioned.

A causal inference involves drawing conclusions about cause-effect relationships between events. For example, after reading "The actress fell from the 14th floor," readers might infer she died, even though this outcome wasn't stated. Studies show people who focus on contextual elaboration recognize words like "DEAD" faster after such sentences, demonstrating how we automatically generate causal inferences.

What is syntax? How does it contribute to our understanding and interpreting of meaning?

Syntax refers to the rules for combining words into grammatically correct sentences. It contributes significantly to meaning by establishing relationships between words that determine "who did what to whom." Even when individual words are understood, improper syntax can obscure meaning (e.g., "Close election the was very").

Syntax guides interpretation through structural cues that reduce ambiguity. For example, in "Put the apple on the towel in the box," the ambiguous prepositional phrase causes momentary confusion about whether "on the towel" describes the apple's location or destination. However, adding "that's" ("Put the apple that's on the towel in the box") clarifies the syntax and eliminates confusion, as shown by eye tracking studies where participants fixate less on incorrect locations.

Research shows we maintain syntactic structures between sentences, making processing easier when similar structures are repeated. Switching between different syntactic structures requires more cognitive effort and slows reading time. Interestingly, visual-spatial experiences can prime syntactic processing, suggesting syntactic representations aren't purely linguistic but may involve spatial components, demonstrating how deeply syntax influences our understanding of language.

• In the context of problem solving, what is a problem? What is something that would not be a problem?

A problem is a situation in which there is an obstacle between a present state and a goal, and it is not immediately obvious how to get around the obstacle. It involves three key components: a current state, a desired state, and no immediate operations or solutions to bridge the gap between them.

Something would not be a problem if:

• The path from current state to goal state is immediately obvious

• There are no obstacles between the present state and goal

• A routine or automatic procedure exists that can be applied without additional thinking

• What is trial and error? What is meant by Thorndike's "Law of Effect"? When is trial and error useful? Not useful?

Trial and error is a problem-solving approach where various actions are attempted until a solution is found. The person tries different possibilities without necessarily having a structured plan, continuing until they find what works.

Thorndike's "Law of Effect" refers to how behaviors that lead to satisfying outcomes are "stamped in" or reinforced, making them more likely to be repeated in similar situations in the future. This is how animals (and humans) learn which actions are effective through reinforcement.

Trial and error is useful when:

• You don't need much information about the problem beforehand

• The problem is simple with few possible actions

• You're only one step away from solving the problem

Trial and error is not useful when:

• Problems have many possible actions (like a Rubik's cube)

• There are many intermediate states between current and goal states

• Problems require multiple subgoals to be achieved in sequence

• Problems have nearly infinite options (like social situations)

• What is the nature of insight problem solving? What is the pattern of how close someone feels to solving the problem when they use insight based problem solving? What is meant by restructuring and how does restructuring the problem representation lead to insight?

Insight problem solving involves the sudden realization of a problem's solution after a period of being stuck. It's characterized by an "aha" or "eureka" moment where the solution becomes clear all at once.

The pattern of how close someone feels to solving an insight problem is typically "cold, cold, cold, cold" until suddenly correct. Unlike gradual approaches where people feel they're getting warmer, with insight problems people report feeling completely stuck until the moment of solution.

Restructuring means thinking about the problem in a fundamentally different way. The process typically follows these phases:

1. Initial search through wrong representation (feeling cold because you're thinking about the problem incorrectly)

2. Impasse – becoming stuck and unable to progress

3. Restructuring – reframing how you understand the problem and its constraints

4. Sudden solution – the answer becomes clear once the right restructuring is found

Restructuring leads to insight by breaking free from incorrect assumptions or constraints, allowing you to see new possibilities that were previously invisible due to how you were framing the problem.

• Why is suppression relevant for problem solving? How would someone who struggles with suppression perform on a problem solving task? 

Suppression is relevant for problem solving because it allows you to inhibit irrelevant or misleading information and overcome fixation on incorrect approaches. It helps you ignore prepotent but unproductive responses to focus on potentially useful solutions.

Someone who struggles with suppression would:

• Have difficulty overcoming initial incorrect associations or approaches

• Get stuck on their first idea or strategy, even when it's not working

• Be unable to break away from conventional uses of objects

• Struggle particularly with insight problems that require restructuring

• Show high functional fixedness and mental set fixation

• Have trouble with creative problem solving that requires generating novel uses for familiar objects

• What is meant by functional fixedness? How does it affect your performance when you are trying to solve problems? What can be done to reduce functional fixedness?

Functional fixedness is the cognitive bias that limits a person to using an object only in the way it is traditionally or commonly used. It's fixation on an item having one particular use, making it difficult to see alternative uses.

Effects on problem-solving performance:

• Prevents seeing novel uses for familiar objects

• Limits creativity and flexibility in approaching problems

• Makes insight problems particularly difficult when they require using objects in unconventional ways

To reduce functional fixedness:

• Actively work against prior associations by deliberately considering alternative uses

• Use inhibitory processing to suppress the common association

• Work with novel objects that don't have established functions or associations

• Practice divergent thinking exercises that involve generating multiple uses for objects

• Focus on the properties of objects rather than their typical functions

• Break objects down into their components rather than viewing them as unified wholes

• What is fixation of a mental set? How does it affect your performance when you are trying to solve problems? What can be done to reduce fixation of a mental set?

Fixation of a mental set is when people persist in using problem-solving methods that worked in the past, even when these methods are no longer efficient or appropriate for the current problem. It's being locked into a particular approach or way of thinking.

Effects on problem-solving performance:

• Makes it difficult to try new approaches even when the current method isn't working

• Leads to inefficient solution paths

• Causes people to miss simpler or more efficient solutions

• Creates unnecessary constraints in how you approach new problems

To reduce fixation of mental set:

• Take breaks to allow your mind to reset (incubation period)

• Explicitly identify and question your assumptions

• Ask "What am I assuming that I don't need to assume?"

• Try deliberately using different approaches than you would normally use

• Consider how someone from a different background might approach the problem

• Expose yourself to diverse problem-solving methods

• Practice mindfulness of your thinking processes

• What is meant by algorithmic thinking? Why does it require more information than trial and error? How do goals and planning contribute to algorithmic thinking? 

Algorithmic thinking involves following a specific sequence of well-defined actions to achieve a desired outcome. It's a systematic approach that follows clear steps from the initial state to the goal state.

It requires more information than trial and error because:

• You need to know potential operations/actions in advance

• You need to understand how each action affects the problem state

• You need knowledge of the problem space to plan the sequence of actions

• You need to be able to predict outcomes of actions before taking them

Goals and planning contribute to algorithmic thinking through:

1. Goal directedness - Having a clear endpoint provides direction for the algorithm

2. Planning a sequence of operations - Mapping out steps before executing them

3. Implementation of operations - Following the planned sequence

4. Subgoal decomposition - Breaking down complex goals into manageable parts

5. Creating a problem space that defines all possible states and transitions between them

• What is means ends analysis? Why do people use subgoals rather than algorithmically planning the steps between the initial state and goal state? Is reaching a subgoal rewarding? 

Means-ends analysis is a heuristic approach to problem solving where you:

1. Identify the difference between the current state and goal state

2. Find an operator (action) that reduces this difference

3. Create subgoals to make the problem more manageable

People use subgoals rather than complete algorithmic planning because:

• Full planning becomes exponentially complex and cognitively taxing

• The problem space grows too large for complete search

• Working memory constraints limit how many steps can be planned ahead

• Subgoals reduce the problem space, making search more manageable

• Subgoals provide intermediate feedback on progress

Yes, reaching a subgoal is rewarding. Neuroimaging evidence shows that unexpected reduction in time to reach a subgoal activates reward centers in the brain (anterior cingulate cortex and insula), even when the overall time to solution remains the same. This suggests that humans find satisfaction in achieving intermediate progress markers.

• What are repeat state avoidance and difference reduction? Why do these heuristics of problem solving sometimes lead to bad outcomes?

Repeat state avoidance (backup avoidance) is a heuristic where people avoid taking actions that return them to a previous state in the problem space. They have an aversion to moves that seem like they're going backward.

Difference reduction is a heuristic where people prefer actions that lead to the greatest similarity between the current state and goal state. People don't like taking steps that move them away from the final state.

These heuristics sometimes lead to bad outcomes because:

• Some problems require temporarily increasing the difference from the goal

• Some optimal solutions require revisiting previous states

• They can lead to local minima where progress stalls

• They can prevent exploration of seemingly counterintuitive but necessary moves

• They bias against moves that appear to be detours but are actually shortcuts

• They create tunnel vision, focusing only on immediate progress

• What is structure mapping theory in the context of problem solving with analogies?

Structure mapping theory explains how analogies work in problem solving. It states that when using an analogy, we:

1. Find corresponding relations between the source (familiar) and target (unfamiliar) domains

2. Map the structural relationships from the source to the target

3. Focus on systematicity - coherent systems of relations rather than isolated features

The theory distinguishes between:

• Alignable relationships: corresponding relations between source and target ("a spider has more eyes than a dog")

• Non-alignable relationships: relations with no correspondence ("a spider can create webs, but dogs cannot")

The process involves:

1. Noticing the potential analogy

2. Mapping the source problem to the target problem

3. Applying the mapping to generate a solution

According to the theory, we find correspondences between relations in the source and target domains, focusing on systematic relationships to transfer knowledge from a familiar situation to a novel one.

• What are surface similarities? Structural similarities? How do each contribute to problem solving? 

Surface similarities are superficial features that two problems share, such as similar objects, settings, characters, or terminology. They're immediately noticeable but don't necessarily relate to the solution method.

Structural similarities are shared relationships, principles, or solution methods between problems. They involve the underlying causal or logical structure that determines how the problem should be solved.

Contributions to problem solving:

Surface similarities:

• Help with noticing potential analogies (initial recognition)

• Make it more likely that someone will attempt to transfer knowledge

• Can be misleading if not accompanied by structural similarity

• Most useful in the early stages of problem identification

Structural similarities:

• Enable effective transfer of solution methods

• Support deeper understanding of problem types

• Allow application of existing knowledge to novel situations

• More useful for actually solving the problem correctly

• Lead to recognition of problem categories or schemas

The ideal situation is when both surface and structural similarities are high, as this makes analogies easier to notice and apply correctly. However, surface similarity without structural similarity can lead to inappropriate application of solution methods.

• Why do experts differ from novices in terms of problem solving?

Experts differ from novices in problem solving in several key ways:

1. Knowledge organization:

   • Experts organize knowledge around fundamental principles and structural features

   • Novices focus more on surface features and literal problem descriptions

2. Problem representation:

   • Experts spend more time analyzing and understanding problems before attempting solutions

   • Novices jump to solution attempts more quickly without full problem analysis

3. Pattern recognition:

   • Experts recognize meaningful patterns and problem types based on deep structural features

   • Novices categorize problems based on superficial similarities

4. Working memory efficiency:

   • Experts use chunking to handle more information in working memory

   • Experts have automated many aspects of problem solving, freeing up cognitive resources

5. Analogical reasoning:

   • Experts are better at noticing structural similarities between problems despite surface differences

   • Experts more effectively transfer appropriate solution methods across domains

6. Solution approaches:

   • Experts work forward from principles to solutions

   • Novices often work backward from goals using means-ends analysis

These differences allow experts to solve problems more efficiently, accurately, and flexibly than novices, particularly in their domain of expertise.

• What is the difference between judgment and decision making?

Judgment is the evaluation process where we assess options, determine their value, or estimate the likelihood of outcomes. Decision making follows judgment and involves making a choice based on those evaluations. For example, when choosing between a burger and asparagus, judgment involves evaluating taste, health benefits, price, and accessibility of each option, while decision making is the actual selection between the two based on those judgments.

• What is the difference between inductive reasoning and deductive reasoning?

Deductive reasoning (like Aristotle's approach) is theory-based, top-down inference that produces conclusions that are either true or false. It starts with general premises and moves to specific conclusions that must be true if the premises are true.

Inductive reasoning (like Sherlock Holmes' approach) is data-based, bottom-up inference that produces probabilistic conclusions. It starts with specific observations and moves to broader generalizations that are likely but not guaranteed to be true. Inductive reasoning involves making predictions based on patterns in data.

• What is a syllogism? What does it mean for a syllogism to be valid? Invalid? What is belief bias and what role do our prior beliefs play in determining whether we think a syllogism is valid or invalid?

A syllogism is a form of deductive reasoning with:

• Major premise (All A are B)

• Minor premise (C is A)

• Conclusion (Therefore, C is B)

Example: All artists are creative (major). Faith is an artist (minor). Therefore, Faith is creative (conclusion).

A syllogism is valid if the conclusion logically follows from the premises, regardless of whether the premises are actually true. A syllogism is invalid if the conclusion does not logically follow from the premises.

Belief bias is our tendency to judge a syllogism as valid if its conclusion seems believable, regardless of its actual logical validity. Our prior beliefs make us more likely to accept an invalid syllogism with a believable conclusion than a valid syllogism with an unbelievable conclusion. Research shows that when conclusions are believable, people are less sensitive to logical validity, and right inferior frontal gyrus activity (associated with inhibition) increases when correctly evaluating syllogisms with unbelievable conclusions.

• What is a conditional syllogism? 

A conditional syllogism is an "If... then" statement with four possible forms:

1. Affirming the antecedent (valid): If p then q, p, therefore q (97% of people get this correct) Example: If it snows, the air is cold. It is snowing. Therefore, the air is cold.

2. Denying the consequent (valid): If p then q, not q, therefore not p (60% of people get this correct) Example: If it snows, the air is cold. The air is not cold. Therefore, it is not snowing.

3. Affirming the consequent (invalid): If p then q, q, therefore p (only 40% of people recognize this is incorrect) Example: If it snows, the air is cold. The air is cold. Therefore, it is snowing.

4. Denying the antecedent (invalid): If p then q, not p, therefore not q (only 40% of people recognize this is incorrect) Example: If it snows, the air is cold. It is not snowing. Therefore, the air is not cold.

• What does the Wason card task tell us about judgment? Why is it hard? Why does a falsification strategy help us? Why does using real world rules (rather than arbitrary letters and numbers) help us?

The Wason card task reveals our poor deductive reasoning skills, particularly our difficulty with conditional reasoning. It's hard because we tend to look for confirming evidence (confirmation bias) rather than disconfirming evidence that would falsify a rule.

A falsification strategy helps because it focuses on finding counterexamples that could prove the rule false, which is the most efficient way to test a conditional rule. For example, when testing "if P then Q," looking for instances of P and not Q is the most informative approach.

Using real-world rules helps because we're better at reasoning when working with familiar contexts and social norms. We draw on our everyday experience with social contracts and violations, which makes the logical structure harder for us to see, showing that familiarity and schemas affect our reasoning.

• Why do people use heuristics, even thought they are prone to systematic errors? (System 1 and 2?)

People use heuristics (mental shortcuts) despite their systematic errors because:

1. They save cognitive effort and time (System 1 thinking is fast)

2. They allow us to make judgments without needing all information

3. They work sufficiently well in many everyday situations

4. We often don't need perfect accuracy for most decisions

5. The cognitive cost of algorithmic thinking (System 2) is high

Even though heuristics sometimes lead to irrational judgments and biases, their efficiency makes them worthwhile for most daily decisions when precision isn't critical.

• What is the availability heuristic? Why might familiarity, salience, and recency contribute to errors in judgments of frequency and likelihood? 

The availability heuristic is estimating the frequency or probability of an event based on how easily examples come to mind. We judge events as more likely when instances are more mentally "available."

This heuristic leads to errors because:

• Familiarity: We overestimate frequencies of things we're familiar with, regardless of their actual occurrence

• Salience: Vivid, dramatic, or emotional events are remembered better and seem more common (like thinking New York is more dangerous because of dramatic news stories)

• Recency: Recent events are easier to recall and therefore judged as more frequent or likely

These factors distort our perception of probability by making certain events more mentally accessible than others, leading to systematic biases in risk perception and frequency estimation.

• What is the representativeness heuristic? How does it relate to base rate neglect & the conjunction fallacy? (LINDA PROB)

The representativeness heuristic judges the likelihood that something belongs to a category based on how similar it appears to our mental prototype of that category. We ask "does this seem like it belongs to category X?" rather than considering statistical probability.

Base rate neglect occurs when we ignore the prior probability (base rate) of something occurring and focus instead on how representative it seems. For example, if someone fits the stereotype of a librarian, we might ignore the fact that librarians are rare in the general population.

The conjunction fallacy occurs when we judge a specific combination of events (A and B) as more likely than a single broader event (A), which is mathematically impossible. In the "Linda problem," people rate "Linda is a bank teller and active in the feminist movement" as more likely than "Linda is a bank teller" because the combination seems more representative of Linda based on her description, ignoring the fact that a conjunction cannot be more probable than either of its components alone.

• What is anchoring and adjustment?

Anchoring and adjustment is a heuristic where people make estimates by starting with an initial value (the anchor) and then adjusting insufficiently away from it. The adjustment typically doesn't move far enough from the initial anchor, leading to biased estimates.

In a classic experiment, people spun a wheel of fortune (giving random numbers 1-100) before estimating the percentage of African nations in the UN. Those who saw "10" estimated around 25%, while those who saw "65" estimated around 45%. The initial random number (anchor) significantly influenced their final judgments, even though it was completely irrelevant to the question.

This heuristic affects negotiations, pricing decisions, and many quantitative judgments in daily life.

• What is expected utility theory? What is meant by rationality, transitivity, an consistence? How do the decoy effect, endowment effect, and optimism bias differ from expected utility theory?

Expected utility theory states that people make decisions by choosing options that maximize their utility (satisfaction/pleasure). According to this theory:

• Rationality means always preferring the option with the highest utility

• Transitivity means if you prefer A over B and B over C, you must prefer A over C

• Consistence means if you prefer A over B now, you should still prefer A over B later (under the same conditions)

However, actual behavior often violates these principles:

• The decoy effect violates consistence - adding an irrelevant third option can change preferences between original options

• The endowment effect violates rationality - people value goods more simply because they own them (owners set higher selling prices than buyers' purchasing prices for identical items)

• Optimism bias violates rationality - unexpected positive events (like sports team wins) increase risk-taking behavior (like buying lottery tickets), showing that mood affects decisions independently of expected utility

These effects demonstrate that human decision-making systematically deviates from the rational model proposed by expected utility theory.

• What is prospect theory? What is meant by a gain frame VS a loss frame? When are people more accepting of taking a risk and why?

Prospect theory (developed by Kahneman and Tversky) explains how people evaluate potential losses and gains. Key concepts include:

• People experience diminishing returns (utility doesn't increase linearly with gains)

• Losses weigh heavier than equivalent gains (loss aversion)

A gain frame presents a decision in terms of what could be gained (e.g., "save 200 lives"), while a loss frame presents the same decision in terms of what could be lost (e.g., "400 people will die").

People are more accepting of taking risks when decisions are framed as losses. When trying to avoid losses, people become risk-seeking, while when pursuing gains, people are risk-averse. For example, when faced with a choice between a sure gain of $50 or a 50% chance of winning $100, most people choose the sure gain. However, when faced with a sure loss of $50 or a 50% chance of losing $100, most people choose the risky option.

This occurs because the pain of losses is psychologically more powerful than the pleasure of equivalent gains.

• What is delay discounting? How do choices about willingness do delay discount relate to being under greater cognitive load? How do choices about willingness to delay discount relate to the degree to which people can suppress an intuitive answer and give a reflective answer?

Delay discounting refers to the tendency to devalue rewards that are delayed in time—the longer the wait for a reward, the less valuable it becomes. This explains why people often prefer smaller immediate rewards over larger delayed rewards.

Under greater cognitive load (when mental resources are taxed), people show stronger delay discounting. When cognitive resources are limited, people are more likely to choose immediate smaller rewards rather than waiting for larger later rewards.

People who perform better on the Cognitive Reflection Test (ability to suppress intuitive but incorrect answers in favor of reflective correct ones) show less steep delay discounting. This suggests that the ability to override automatic responses (System 1) with deliberate reasoning (System 2) is linked to greater patience and willingness to wait for larger rewards.

The marshmallow test with children demonstrates this concept—children who can delay gratification show better outcomes later in life regarding SAT scores, educational attainment, and response inhibition decades later.

• What is an interference task? What is the target/goal information and the compelling distractor in Stroop task? Flanker task? Simon task? What does it mean for a trial to be congruent? Incongruent?

Interference tasks measure cognitive control by requiring participants to respond to relevant information while ignoring distracting information. In these tasks:

1. Stroop Task:

   • Goal: Name the color of the ink

   • Compelling distractor: The word itself (reading is automatic)

   • Congruent: Word and ink color match (e.g., "RED" in red ink)

   • Incongruent: Word and ink color differ (e.g., "RED" in blue ink)

2. Flanker Task:

   • Goal: Respond to the direction of the middle arrow

   • Compelling distractor: Surrounding arrows

   • Congruent: All arrows point in same direction (→→→→→)

   • Incongruent: Middle arrow differs from surrounding arrows (→→←→→)

3. Simon Task:

   • Goal: Respond to the color (e.g., blue=left button, green=right button)

   • Compelling distractor: Spatial location of stimulus

   • Congruent: Response side matches stimulus location (blue on left)

   • Incongruent: Response side differs from stimulus location (blue on right)

In all cases, congruent trials have target and distractor information suggesting the same response, while incongruent trials have them suggesting different responses, creating response conflict that requires cognitive control to resolve.

• Why is word reading information so compelling in the context of a Stroop task? 

Word reading is compelling in the Stroop task because:

1. It's highly practiced and automated through years of daily reading

2. It's processed faster than color identification

3. It occurs automatically without intentional effort

4. It's typically stronger than color naming due to extensive practice

5. In daily life, we read words for their meaning, not their physical attributes

This automaticity means that word reading occurs involuntarily even when participants are instructed to ignore the words and focus only on colors. Without cognitive control, the automatic response (reading the word) wins out over the goal-relevant response (naming the color), explaining why incongruent trials are more difficult and require more time.

• What is task switching? Why does it require cognitive control?

Task switching refers to shifting from one cognitive task to another. For example, switching between categorizing objects by shape to categorizing them by color.

Task switching requires cognitive control because:

1. The previous task must be inhibited (suppressing the old task set)

2. The new task must be activated (implementing the new rules)

3. Task inertia must be overcome (mental "momentum" from previous task)

4. Working memory must be updated with new task rules

5. Representation must be reconfigured from one task to another

This process is cognitively demanding and typically results in a "switch cost" (slower response times and higher error rates on switch trials compared to repeated-task trials). These costs occur because cognitive control processes needed for updating task representations and suppressing irrelevant task sets require time and mental effort.

When there are only a few tasks, both can be maintained in working memory, but this introduces additional control demands to suppress the currently irrelevant task.

• What is the difference between reactive and proactive control? What is the AX-CPT task, and how does it differentiate between proactive and reactive control? 

Reactive control is activated "in the moment" when interference or conflict is encountered. It responds to perceived elements after they appear and requires a triggering event. Proactive control is anticipatory and preparatory, maintaining goals before control is needed. It's stimulus-independent and involves planning ahead for potential conflict.

AX-CPT Task: The AX-CPT (AX-Continuous Performance Task) differentiates between these control types by the rule: "respond to X only when it follows A." The task reveals different error patterns:

• Proactive control users make more errors on AY trials (responding to Y after A) because they strongly maintain the "A" probe.

• Reactive control users make more errors on BX trials (responding to X after B) because they react to the X without maintaining context.

• People with higher working memory capacity show more proactive control patterns: fewer BX errors but slower on AY trials due to maintaining the "A" context.

• What is meant by guided activation? How does the conflict monitoring account propose that frontal lobe areas intervene between perceiving a stimulus and responding to help us adjust to conflict?

Guided activation theory explains how prefrontal cortex implements cognitive control by acting as an intermediary between stimulus perception and motor response. It accounts for goals, rules, and context to activate appropriate responses - essentially changing the "railroad tracks" of neural processing to guide behavior toward goal-relevant responses.

Conflict Monitoring Account: According to this account, frontal lobe areas (specifically ACC and DLPFC) detect and resolve conflict:

1. ACC (Anterior Cingulate Cortex) monitors for conflict between competing responses

2. DLPFC (Dorsolateral Prefrontal Cortex) resolves conflict by enhancing goal-relevant responses

3. When conflict is detected, the system signals the need for control, which then suppresses irrelevant information (like word reading in the Stroop task) and enhances goal-relevant processing (like color naming).

This intervention happens between perception and response, allowing us to adjust our behavior based on detected conflict.

• If control is useful in helping us achieve our goals, why do we not engage cognitive control all the time?

Despite its usefulness, we don't engage cognitive control all the time because:

1. It's cognitively demanding and effortful

2. It creates "tunnel vision" that filters out potentially useful information

3. Control carries an intrinsic cost that is weighed against potential rewards

4. Following the "law of least mental effort," we prefer less effortful sequences when rewards are equal

5. We engage in effort discounting - the perceived value of rewards decreases as effort requirements increase

We strategically allocate control resources based on cost-benefit analysis rather than maximizing control constantly.

• What is ego depletion theory? What does it predict about will power as a resource, and what does it predict about what happens when that resource is depleted?

Ego Depletion Theory: This theory proposes that willpower is a limited resource that can be depleted through use. According to this view:

• Willpower/self-control is like a muscle that fatigues with use

• The proposed resource was blood glucose (the brain's main energy source)

• Exerting willpower in one task leaves less available for subsequent tasks

• Willpower starts fresh each day and depletes over time

Predictions:

1. After exerting self-control (like resisting cookies), performance on subsequent unrelated self-control tasks (like solving puzzles) will decline

2. Decision fatigue occurs after making many choices

3. Replenishing glucose (through sugary drinks) should restore self-control capacity

4. The depletion effect should not be overcome by motivation alone

• What arguments exist against ego depletion? What is the expected value of control account, and why difference does it make in terms of prediction in comparison to the ego depletion account? 

1. Non-sugar beverages can eliminate the depletion effect, contradicting the glucose explanation

2. Rewarding participants for performance eliminates depletion effects, suggesting motivation can overcome supposed resource limitations

3. Methodological issues in studies (e.g., the judge parole study didn't control for meal timing)

4. Poor replication of ego depletion studies

Expected Value of Control Account: This alternative explanation proposes that:

1. Control exertion is treated as a cost that is weighed against potential benefits

2. People strategically allocate control based on cost-benefit analysis

3. When rewards are equal, people choose less effortful options (law of least mental effort)

4. When rewards are sufficient, people will choose higher-effort tasks

Difference in Predictions: Unlike ego depletion, the expected value account predicts:

• Control isn't absolutely limited but is allocated based on perceived value

• People can overcome "depletion" if motivation/rewards are sufficient

• Effort allocation is a choice rather than a resource limitation

• Similar to physical effort, mental effort follows a cost-benefit logic

• What is the difference between fluid abilities and crystallized abilities across the lifespan? On what kind of tasks would you expect an older adult to outperform a younger adult?

• Fluid abilities: Processing speed, working memory, inhibition, long-term memory formation, cognitive flexibility

• Crystallized abilities: Word knowledge, vocabulary, accumulated information

Lifespan Trajectory:

• Fluid abilities generally decline with age

• Crystallized abilities remain stable or improve with age

Tasks where older adults outperform younger adults:

1. Vocabulary tests

2. General knowledge assessments

3. Tasks relying on accumulated wisdom

4. Tasks benefiting from emotional regulation (positivity bias)

5. Tasks where experience can compensate for processing speed

6. Recognition memory tasks with meaningful information

• What is meant by selection, optimization, and compensation in cognitive aging?

Selection, Optimization, and Compensation (SOC) represents adaptive strategies used to adjust to cognitive changes with age:

1. Selection: Choosing to focus on cognitive abilities that remain strong while avoiding tasks that rely on declining abilities. For example, an older adult might select activities that leverage their verbal knowledge rather than tasks requiring rapid processing.

2. Optimization: Investing time and resources to maintain or improve already strong abilities. This might include practicing skills, developing routines, or creating environmental supports that enhance performance in selected domains.

3. Compensation: Using alternative methods, external aids, or intact skills to work around cognitive declines. Examples include using calendars for memory support or leveraging experience to compensate for slower processing speed.

These strategies allow older adults to maintain high levels of functioning despite cognitive changes, demonstrating the remarkable flexibility of human cognition throughout the lifespan.

• What is the inhibitory deficit theory of aging? Why would inhibition affect cognitive aging during inhibition tasks? Why might it have affects outside of inhibition tasks (for example, in a task of working memory or long term memory)

Inhibitory Deficit Theory: This theory proposes that cognitive changes in aging are primarily attributable to difficulty inhibiting irrelevant information. Older adults struggle more with suppressing distracting information or prepotent responses.

Effects on Inhibition Tasks: During inhibition tasks like the Stroop test, older adults show greater difficulty not reading words compared to younger adults. They're particularly impaired in proactive control (preparing to inhibit information) while reactive control remains relatively intact.

Effects Beyond Inhibition Tasks: Poor inhibition affects other cognitive domains:

1. Working memory: Irrelevant information occupies limited capacity

2. Long-term memory: Difficulty filtering distractions during encoding and retrieval

3. Comprehension: Interference from irrelevant associations

4. Decision-making: Inability to suppress irrelevant options

5. Problem-solving: Difficulty inhibiting incorrect strategies

For example, older adults show more answers influenced by to-be-inhibited content in memory tests, suggesting they can't effectively suppress irrelevant information.

• What is the processing speed theory of aging? Why would speed of processing affect cognitive aging during a processing speed task? Why might it have affects outside of inhibition tasks (for example, in a task of working memory or long term memory).

Processing Speed Theory: This theory proposes that most age-related cognitive differences stem from general slowing in the baseline time needed for neural processing and information transmission. Processing speed acts as a fundamental component underlying other cognitive abilities.

Effects on Processing Speed Tasks: On direct speed measures, older adults show consistent slowing across various tasks requiring rapid responses or information processing.

Effects Beyond Processing Speed Tasks: Slowed processing affects other cognitive domains:

1. Working memory: Information decay before processing completes

2. Long-term memory: Incomplete encoding due to time constraints

3. Problem-solving: Fewer operations completed in available time

4. Reasoning: Inability to maintain earlier results while processing later steps

5. Language comprehension: Difficulty integrating information across sentences

Evidence supporting this theory includes the significant reduction in age-cognition correlations when processing speed is statistically controlled for, suggesting that many "fluid intelligence" declines are largely speed-related.

• What is prospective memory? When do older adults perform worse than younger adults? When do older adults perform just as well as younger adults?

Prospective Memory: Prospective memory is remembering to carry out planned actions or intentions in the future - essentially remembering to remember. It represents a crucial aspect of everyday functioning.

When Older Adults Perform Worse: Older adults show significant impairment in time-based prospective memory tasks that require internal generation of reminders without environmental cues. For example, remembering to take medication at specific times or call someone at 3 PM requires self-initiated retrieval with no external reminders.

When Older Adults Perform Similarly: Older adults perform at comparable levels to younger adults (and sometimes better) on event-based prospective memory tasks where environmental cues trigger the memory. For example, remembering to deliver a message when seeing a colleague or buy eggs when passing a grocery store. The external cue in the environment compensates for retrieval difficulties.

This pattern aligns with older adults' general difficulty with self-initiated retrieval processes while their recognition abilities remain relatively preserved.

• What is positivity bias and what does it predict about how older adults will attend to and remember emotionally valanced stimuli?

Positivity Bias: Positivity bias refers to older adults' tendency to preferentially attend to and remember positive information while directing attention away from negative stimuli. This represents an age-related shift in emotional processing.

Predictions about Attention and Memory: The positivity bias predicts that older adults will:

1. Allocate more attention to positive than negative stimuli (unlike younger adults who often show negativity bias)

2. Remember positive information better than negative information

3. Show reduced brain activity when processing negative emotional stimuli

4. Experience fading negative feelings associated with memories faster than positive feelings

5. Reconstruct autobiographical memories with a more positive tone

This bias may contribute to the paradoxical finding that despite physical and cognitive challenges, subjective well-being tends to be highest among older adults. The positivity effect appears to represent an adaptive emotion regulation strategy rather than a cognitive deficit.